mirror of https://github.com/hpcaitech/ColossalAI
286 lines
12 KiB
Python
286 lines
12 KiB
Python
|
from typing import Callable, Iterable, Iterator, List, Optional, Tuple, Union
|
||
|
|
|
||
|
|
import torch
|
||
|
|
import torch.nn as nn
|
||
|
|
from packaging import version
|
||
|
|
from torch.distributed import ProcessGroup
|
||
|
|
|
||
|
|
if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse(
|
||
|
|
torch.__version__) < version.parse('2.0.0'):
|
||
|
|
from torch.distributed.fsdp import FullStateDictConfig
|
||
|
|
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
|
||
|
|
from torch.distributed.fsdp import StateDictType
|
||
|
|
from torch.distributed.fsdp.fully_sharded_data_parallel import (
|
||
|
|
BackwardPrefetch,
|
||
|
|
CPUOffload,
|
||
|
|
MixedPrecision,
|
||
|
|
ShardingStrategy,
|
||
|
|
)
|
||
|
|
elif version.parse(torch.__version__) >= version.parse('2.0.0'):
|
||
|
|
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
|
||
|
|
from torch.distributed.fsdp._init_utils import ProcessGroupType
|
||
|
|
from torch.distributed.fsdp.api import (
|
||
|
|
BackwardPrefetch,
|
||
|
|
CPUOffload,
|
||
|
|
FullOptimStateDictConfig,
|
||
|
|
FullStateDictConfig,
|
||
|
|
MixedPrecision,
|
||
|
|
ShardingStrategy,
|
||
|
|
StateDictType,
|
||
|
|
)
|
||
|
|
from torch.distributed.fsdp.wrap import _FSDPPolicy
|
||
|
|
else:
|
||
|
|
raise RuntimeError("FSDP is not supported while torch version under 1.12.0.")
|
||
|
|
|
||
|
|
from torch.optim import Optimizer
|
||
|
|
from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
|
||
|
|
from torch.utils.data import DataLoader
|
||
|
|
|
||
|
|
from colossalai.checkpoint_io import CheckpointIO, GeneralCheckpointIO
|
||
|
|
from colossalai.cluster import DistCoordinator
|
||
|
|
from colossalai.interface import ModelWrapper, OptimizerWrapper
|
||
|
|
|
||
|
|
from .dp_plugin_base import DPPluginBase
|
||
|
|
|
||
|
|
__all__ = ['TorchFSDPPlugin']
|
||
|
|
|
||
|
|
|
||
|
|
class TorchFSDPCheckpointIO(GeneralCheckpointIO):
|
||
|
|
|
||
|
|
def __init__(self) -> None:
|
||
|
|
super().__init__()
|
||
|
|
self.coordinator = DistCoordinator()
|
||
|
|
|
||
|
|
def __set_model_optim_state(
|
||
|
|
self,
|
||
|
|
model,
|
||
|
|
state_dict_type,
|
||
|
|
state_dict_config,
|
||
|
|
optim_state_dict_config,
|
||
|
|
):
|
||
|
|
return FSDP.set_state_dict_type(model, state_dict_type, state_dict_config, optim_state_dict_config)
|
||
|
|
|
||
|
|
def load_sharded_model(self, model: nn.Module, checkpoint: str):
|
||
|
|
|
||
|
|
# TODO(jishaomin): implement this method as it can be supported by Huggingface model
|
||
|
|
raise NotImplementedError("Torch FSDP sharded model checkpoint is not supported yet.")
|
||
|
|
|
||
|
|
def load_sharded_optimizer(self, model: nn.Module, optimizer: Optimizer, checkpoint: str):
|
||
|
|
|
||
|
|
# TODO(jishaomin): implement this method as it can be supported by Huggingface model
|
||
|
|
raise NotImplementedError("Torch FSDP sharded model checkpoint is not supported yet.")
|
||
|
|
|
||
|
|
def save_sharded_model(self, model: nn.Module, checkpoint: str):
|
||
|
|
|
||
|
|
# TODO(jishaomin): implement this method as it can be supported by Huggingface model
|
||
|
|
raise NotImplementedError("Torch FSDP sharded model checkpoint is not supported yet.")
|
||
|
|
|
||
|
|
def save_sharded_optimizer(self, model: nn.Module, optimizer: Optimizer, checkpoint: str):
|
||
|
|
|
||
|
|
# TODO(jishaomin): implement this method as it can be supported by Huggingface model
|
||
|
|
raise NotImplementedError("Torch FSDP sharded model checkpoint is not supported yet.")
|
||
|
|
|
||
|
|
def load_unsharded_model(self, model: nn.Module, checkpoint: str):
|
||
|
|
"""
|
||
|
|
Load model from checkpoint with automatic unwrapping.
|
||
|
|
"""
|
||
|
|
# the model should be unwrapped in self.load_model via ModelWrapper.unwrap
|
||
|
|
|
||
|
|
if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse(
|
||
|
|
torch.__version__) < version.parse('2.0.0'):
|
||
|
|
full_state_dict = self.load_state_dict(checkpoint)
|
||
|
|
elif version.parse(torch.__version__) >= version.parse('2.0.0'):
|
||
|
|
full_state_dict = self.load_state_dict(checkpoint)
|
||
|
|
self.__set_model_optim_state(model, StateDictType.FULL_STATE_DICT, FullStateDictConfig(rank0_only=True))
|
||
|
|
full_state_dict = model.state_dict()
|
||
|
|
else:
|
||
|
|
raise RuntimeError("FSDP is not supported while torch version under 1.12.0.")
|
||
|
|
|
||
|
|
model.load_state_dict(full_state_dict)
|
||
|
|
|
||
|
|
def load_unsharded_optimizer(self, model: nn.Module, optim: Optimizer, checkpoint: str):
|
||
|
|
"""
|
||
|
|
Load Optimizer from checkpoint with automatic unwrapping.
|
||
|
|
"""
|
||
|
|
|
||
|
|
if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse(
|
||
|
|
torch.__version__) < version.parse('2.0.0'):
|
||
|
|
optim_full_state_dict = self.load_state_dict(checkpoint)
|
||
|
|
elif version.parse(torch.__version__) >= version.parse('2.0.0'):
|
||
|
|
optim_full_state_dict = self.load_state_dict(checkpoint)
|
||
|
|
FSDP.full_optim_state_dict_to_load(optim_full_state_dict, model, optim)
|
||
|
|
else:
|
||
|
|
raise RuntimeError("FSDP is not supported while torch version under 1.12.0.")
|
||
|
|
|
||
|
|
optim.load_state_dict(optim_full_state_dict)
|
||
|
|
|
||
|
|
def save_unsharded_model(self, model: nn.Module, checkpoint: str):
|
||
|
|
"""
|
||
|
|
Save model to checkpoint but only on master process.
|
||
|
|
"""
|
||
|
|
# the model should be unwrapped in self.load_model via ModelWrapper.unwrap
|
||
|
|
|
||
|
|
if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse(
|
||
|
|
torch.__version__) < version.parse('2.0.0'):
|
||
|
|
cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
|
||
|
|
with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, cfg):
|
||
|
|
model_state_dict = model.state_dict()
|
||
|
|
elif version.parse(torch.__version__) >= version.parse('2.0.0'):
|
||
|
|
self.__set_model_optim_state(model, StateDictType.FULL_STATE_DICT, FullStateDictConfig(rank0_only=True))
|
||
|
|
model_state_dict = model.state_dict()
|
||
|
|
else:
|
||
|
|
raise RuntimeError("FSDP is not supported while torch version under 1.12.0.")
|
||
|
|
self.save_checkpoint(model_state_dict, checkpoint)
|
||
|
|
|
||
|
|
def save_unsharded_optimizer(self, model: nn.Module, optimizer: Optimizer, checkpoint: str):
|
||
|
|
"""
|
||
|
|
Save optimizer to checkpoint but only on master process.
|
||
|
|
"""
|
||
|
|
|
||
|
|
if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse(
|
||
|
|
torch.__version__) < version.parse('2.0.0'):
|
||
|
|
optim_state_dict = FSDP.full_optim_state_dict(model=model, optim=optimizer)
|
||
|
|
elif version.parse(torch.__version__) >= version.parse('2.0.0'):
|
||
|
|
self.__set_model_optim_state(model, StateDictType.FULL_STATE_DICT,
|
||
|
|
FullOptimStateDictConfig(rank0_only=True))
|
||
|
|
optim_state_dict = FSDP.optim_state_dict(model, optimizer)
|
||
|
|
else:
|
||
|
|
raise RuntimeError("FSDP is not supported while torch version under 1.12.0.")
|
||
|
|
self.save_checkpoint(optim_state_dict, checkpoint)
|
||
|
|
|
||
|
|
|
||
|
|
class TorchFSDPModel(ModelWrapper):
|
||
|
|
|
||
|
|
def __init__(self, module: nn.Module, *args, **kwargs) -> None:
|
||
|
|
super().__init__(module)
|
||
|
|
self.module = FSDP(module, *args, **kwargs)
|
||
|
|
|
||
|
|
def unwrap(self):
|
||
|
|
return self.module.module
|
||
|
|
|
||
|
|
|
||
|
|
class TorchFSDPPlugin(DPPluginBase):
|
||
|
|
"""
|
||
|
|
Plugin for PyTorch FSDP.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
>>> from colossalai.booster import Booster
|
||
|
|
>>> from colossalai.booster.plugin import TorchFSDPPlugin
|
||
|
|
>>>
|
||
|
|
>>> model, train_dataset, optimizer, criterion = ...
|
||
|
|
>>> plugin = TorchFSDPPlugin()
|
||
|
|
|
||
|
|
>>> train_dataloader = plugin.prepare_train_dataloader(train_dataset, batch_size=8)
|
||
|
|
>>> booster = Booster(plugin=plugin)
|
||
|
|
>>> model, optimizer, train_dataloader, criterion = booster.boost(model, optimizer, train_dataloader, criterion)
|
||
|
|
|
||
|
|
Args:
|
||
|
|
See https://pytorch.org/docs/stable/fsdp.html for details.
|
||
|
|
"""
|
||
|
|
|
||
|
|
if version.parse(torch.__version__) >= version.parse('1.12.0') and version.parse(
|
||
|
|
torch.__version__) < version.parse('2.0.0'):
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
process_group: Optional[ProcessGroup] = None,
|
||
|
|
sharding_strategy: Optional[ShardingStrategy] = None,
|
||
|
|
cpu_offload: Optional[CPUOffload] = None,
|
||
|
|
auto_wrap_policy: Optional[Callable] = None,
|
||
|
|
backward_prefetch: Optional[BackwardPrefetch] = None,
|
||
|
|
mixed_precision: Optional[MixedPrecision] = None,
|
||
|
|
ignored_modules: Optional[Iterable[torch.nn.Module]] = None,
|
||
|
|
param_init_fn: Optional[Callable[[nn.Module], None]] = None,
|
||
|
|
device_id: Optional[Union[int, torch.device]] = None,
|
||
|
|
sync_module_states: bool = False,
|
||
|
|
):
|
||
|
|
super().__init__()
|
||
|
|
self.fsdp_kwargs = dict(process_group=process_group,
|
||
|
|
sharding_strategy=sharding_strategy,
|
||
|
|
cpu_offload=cpu_offload,
|
||
|
|
auto_wrap_policy=auto_wrap_policy,
|
||
|
|
backward_prefetch=backward_prefetch,
|
||
|
|
mixed_precision=mixed_precision,
|
||
|
|
ignored_modules=ignored_modules,
|
||
|
|
param_init_fn=param_init_fn,
|
||
|
|
device_id=device_id,
|
||
|
|
sync_module_states=sync_module_states)
|
||
|
|
elif version.parse(torch.__version__) >= version.parse('2.0.0'):
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
process_group: ProcessGroupType = None,
|
||
|
|
sharding_strategy: Optional[ShardingStrategy] = None,
|
||
|
|
cpu_offload: Optional[CPUOffload] = None,
|
||
|
|
auto_wrap_policy: Optional[Union[Callable, _FSDPPolicy]] = None,
|
||
|
|
backward_prefetch: Optional[BackwardPrefetch] = BackwardPrefetch.BACKWARD_PRE,
|
||
|
|
mixed_precision: Optional[MixedPrecision] = None,
|
||
|
|
ignored_modules: Optional[Iterable[torch.nn.Module]] = None,
|
||
|
|
param_init_fn: Optional[Callable[[nn.Module], None]] = None,
|
||
|
|
device_id: Optional[Union[int, torch.device]] = None,
|
||
|
|
sync_module_states: bool = False,
|
||
|
|
forward_prefetch: bool = False,
|
||
|
|
limit_all_gathers: bool = False,
|
||
|
|
use_orig_params: bool = False,
|
||
|
|
ignored_parameters: Optional[Iterable[torch.nn.Parameter]] = None,
|
||
|
|
):
|
||
|
|
super().__init__()
|
||
|
|
self.fsdp_kwargs = dict(process_group=process_group,
|
||
|
|
sharding_strategy=sharding_strategy,
|
||
|
|
cpu_offload=cpu_offload,
|
||
|
|
auto_wrap_policy=auto_wrap_policy,
|
||
|
|
backward_prefetch=backward_prefetch,
|
||
|
|
mixed_precision=mixed_precision,
|
||
|
|
ignored_modules=ignored_modules,
|
||
|
|
param_init_fn=param_init_fn,
|
||
|
|
device_id=device_id,
|
||
|
|
sync_module_states=sync_module_states,
|
||
|
|
forward_prefetch=forward_prefetch,
|
||
|
|
limit_all_gathers=limit_all_gathers,
|
||
|
|
use_orig_params=use_orig_params,
|
||
|
|
ignored_parameters=ignored_parameters)
|
||
|
|
else:
|
||
|
|
raise RuntimeError("FSDP is not supported while torch version under 1.12.0.")
|
||
|
|
|
||
|
|
def support_no_sync(self) -> bool:
|
||
|
|
False
|
||
|
|
|
||
|
|
def no_sync(self, model: nn.Module) -> Iterator[None]:
|
||
|
|
raise NotImplementedError("Torch fsdp no_sync func not supported yet.")
|
||
|
|
|
||
|
|
def control_precision(self) -> bool:
|
||
|
|
return True
|
||
|
|
|
||
|
|
def supported_precisions(self) -> List[str]:
|
||
|
|
return ['fp16', 'bf16']
|
||
|
|
|
||
|
|
def control_device(self) -> bool:
|
||
|
|
return True
|
||
|
|
|
||
|
|
def supported_devices(self) -> List[str]:
|
||
|
|
return ['cuda']
|
||
|
|
|
||
|
|
def configure(
|
||
|
|
self,
|
||
|
|
model: nn.Module,
|
||
|
|
optimizer: Optimizer,
|
||
|
|
criterion: Callable = None,
|
||
|
|
dataloader: DataLoader = None,
|
||
|
|
lr_scheduler: LRScheduler = None,
|
||
|
|
) -> Tuple[Union[nn.Module, OptimizerWrapper, LRScheduler, DataLoader]]:
|
||
|
|
|
||
|
|
model = model.cuda()
|
||
|
|
# wrap the model with PyTorch FSDP
|
||
|
|
model = TorchFSDPModel(model, **self.fsdp_kwargs)
|
||
|
|
|
||
|
|
if not isinstance(optimizer, OptimizerWrapper):
|
||
|
|
optimizer = OptimizerWrapper(optimizer)
|
||
|
|
|
||
|
|
return model, optimizer, criterion, dataloader, lr_scheduler
|
||
|
|
|
||
|
|
def control_checkpoint_io(self) -> bool:
|
||
|
|
return True
|
||
|
|
|
||
|
|
def get_checkpoint_io(self) -> CheckpointIO:
|
||
|
|
return TorchFSDPCheckpointIO()
|